Winter commingling of populations of migratory species can cause breeding range underpopulation
Bibliographic record
Abstract
We build a model with large‐scale demographic consequences for migratory species. The model operates where four elements co‐occur, and we rely on empirical research using migratory birds to demonstrate them. First, breeding ranges have internal structure flowing from natal philopatry. Second, fecundity varies geographically. Third, populations of different breeding provenances commingle during winter. And fourth, a population‐limiting carrying capacity operates during winter. In the absence of breeding season population‐limitation, only the breeding population with maximum fecundity persists. Consequently, some potential breeding areas that offer suitable and productive habitat are bereft of breeding birds because of the interplay between the geographical fecundity gradient and the shared winter quarters. Where breeding season population‐limitation also plays a role for at least one population, one (or more) breeding population becomes permanently depressed, resulting in a density well below the carrying capacity of the productive breeding habitat that is occupied. In either case, not all populations fare equally well, despite net positive breeding season productivity. Changes in winter carrying capacity, for example habitat degradation in winter quarters, can lead to uneven effects on geographically defined breeding populations, even though there has been no change in the circumstances of the breeding range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".